Extraction of tire characteristics combining direct tpms and tire resonance analysis

ABSTRACT

Embodiments relate to tire characterization systems and methods for combining direct tire pressure monitoring systems (TPMS) and tire resonance analysis in indirect tire pressure monitoring systems (TPMS) for the extraction of tire characteristics to characterize other tire parameters. In embodiments, iTPMS and methods that utilize anti-lock braking system (ABS) sensed signals coupled to an electronic control unit (ECU) that may comprise circuitry and/or controllers to process the sensed signals using a resonance frequency analysis (RFA) technique can be combined with direct tire pressure sensor measurements from direct TPMS systems. Because direct TPMS systems delieve a precise value of a tire characteristic (for example, tire pressure), one of the unknown parameters that influence the resonance effects can be removed. As a results, the detected resonance can be used to characterize another tire parameter that would not be accessible without knowledge of the precise value of the tire characteristic delivered by the direct TPMS system.

REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.14/047,343 filed on Oct. 7, 2013.

TECHNICAL FIELD

Embodiments relate generally to direct tire pressure monitoring andindirect tire pressure monitoring, and more particularly to combiningdirect tire pressure monitoring and tire resonance analysis for theextraction of tire characteristics to characterize other tireparameters.

BACKGROUND

There are two general approaches to monitoring the pressure in vehicletires: direct and indirect. Direct tire pressure monitoring systemstypically comprise a wheel module having one or more sensors andelectronics mounted in or to the tire to directly measure the tire'spressure and wirelessly transmit measurement data to the vehicle.

Indirect TPMSs generally utilize information from other vehicle sensorsand/or systems to indirectly estimate a tire's pressure without directTPMS sensors or electronics being located in the tire. Indirect TPMS isattractive because it can be more cost-efficient than direct TPMS. Oneconventional indirect TPMS uses wheel speed signals from the anti-lockbrake system (ABS). For a typical passenger vehicle having four tires,the indirect TPMS compares the four wheel speed signals to determinewhether a wheel is rotating faster because of a loss of pressure andrelated decreased diameter. One drawback to some of these indirectsystems is that the systems cannot detect whether all wheels have lostpressure over time because the values are compared.

One approach for overcoming this drawback is to utilize a resonancefrequency method (RFM) of analysis of a single resonance frequency inthe sensed data signals from the ABS. U.S. Pat. Nos. 8,207,839 and8,347,704 describe different kinds of RFM analysis of a time series ofsensed data signals that includes auto-regression analysis, Fast Fourieranalysis, a Bayesian analysis, or analysis based on a linear estimationmodel. While different kinds of analysis are taught by these patents,the purpose of each of these known RFM approaches is to reduce theamount of computation power required in an on-board processor to do thecalculations necessary to identify a single resonance frequency fromwhich tire pressure can be indirectly estimated. While RFM analysis canrepresent an improvement over conventional indirect TPMS, the accuracyof the results can be impacted by the low resolution of the ABS senseddata signals and by other factors that can influence the resonancefrequency beyond just the tire pressure in an individual tire.

Therefore, in another approach, a multidimensional resonance frequencyanalysis (MRFA) that includes a spectral analysis identifying at leasttwo tire vibration modes in the wheel speed signal and isolates at leastone characteristic affecting the at least two tire vibration modes, forexample, that described in U.S. patent application Ser. No. 13/919,620,which is herein incorporated by reference in its entirety, can also beutilized. In MRFA approaches, the number of tire parameters that can beextracted depends on the number of different resonance modes that can beidentified in the spectrum of the wheel speed signal and on asignificant difference of their respective dependence on the differentparameters.

Moreover, typical direct TPMS and indirect TPMS do not interface witheach other. Therefore, there is a need for combined direct tire pressuremonitoring and tire resonance analysis for the extraction of tirecharacteristics in order to characterize other tire parameters.

SUMMARY

In an embodiment, a tire characterization system comprises a sensorconfigured to provide a wheel speed signal; a direct tire pressuremonitoring system (TPMS) including at least one sensor configured toprovide a direct measurement of a characteristic of the tire; and acharacterization engine configured to process the wheel speed signal toidentify at least one tire vibration mode and use the identified atleast one tire vibration mode and the direct measurement to estimate acharacterized parameter of the tire.

In an embodiment, a method of characterizing at least one parameter of avehicle tire, the method comprises obtaining data representative of awheel speed of the tire; analyzing the data; obtaining a directmeasurement from at least one sensor corresponding to the unknownparameter; incorporating the direct measurement into the analyzed datato remove the unknown parameter; and characterizing one or moreadditional parameters of the vehicle tire.

In an embodiment, a tire characterization system comprises at least onesensor configured to provide at least a direct measurement of acharacteristic of the tire; an wheel speed sensor configured to providea wheel speed signal; and an electronic control unit (ECU) coupled tothe wheel speed sensor and configured to process the sensed wheel speedsignal, to identify at least one tire vibration mode, and process thedirect measurement and the identified at least one tire vibration modein order to estimate a characterized parameter of the tire.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be more completely understood in consideration of thefollowing detailed description of various embodiments of the inventionin connection with the accompanying drawings, in which:

FIG. 1A is a block diagram of a tire characterization system, accordingto an embodiment.

FIG. 1B is a block diagram of a tire characterization system, accordingto an embodiment.

FIG. 2 is a schematic diagram of a wheel sensor system and tire,according to an embodiment.

FIG. 3 is a block diagram of an indirect tire pressure monitoring system(TPMS), according to an embodiment.

FIG. 4 is a block diagram of an indirect tire pressure monitoring system(TPMS), according to an embodiment.

FIG. 5 is a graphical representation of a wheel speed signal spectrumanalysis, according to an embodiment.

FIG. 6 is a flowchart of a method, according to an embodiment.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION

Embodiments relate to tire characterization systems and methods forcombining direct tire pressure monitoring systems (TPMS) and tireresonance analysis in indirect tire pressure monitoring systems (TPMS)for the extraction of tire characteristics to characterize other tireparameters. In embodiments, TPMS and methods that utilize anti-lockbraking system (ABS) sensed signals coupled to an electronic controlunit (ECU) that can comprise circuitry and/or controllers to process thesensed signals using a resonance frequency analysis (RFA) technique canbe combined with direct tire pressure sensor measurements from directTPMS systems. Because direct TPMS systems deliever a precise value of atire characteristic (for example, tire pressure), one of the unknownparameters that influence the resonance effects can be removed. As aresults, the detected resonance can be used to characterize another tireparameter that would not be accessible without knowledge of the precisevalue of the tire characteristic delivered by the direct TPMS system. Inembodiments, the addition of one or more direct measurements from thedirect TPMS system into the system of equations that describes thechange of the resonance characteristic generated by the RFA techniquecan remove one or more unknown variables in the RFA technique. Inembodiments, additional direct measurements from the direct TPMS systemcan be incorporated. With the incorporation of additional directmeasurements, additional unknown variables can be removed and additionaltire parameters can be characterized from the system of equations in theRFA technique.

In embodiments, a direct TPMS provides a high quality or highly accuratepressure measurement value. In contrast to indirect TPMS systems,wherein the dependence of the resonance on the pressure must be known,the direct pressure measurement value can be used to eliminate apressure dependence on a resonance parameter. A resonance can thereforebe analyzed for its dependence on another parameter. In embodiments, theresonance can be analyzed for the temperature of the tire material,which influences the friction between the tire and the driving surface.Determining the temperature of the tire material is valuable and isoften not directly measurable due to mechanical assembly challenges withsensors necessarily requiring close contact with the tire material.

In embodiments, in the case that a sensor from the direct TPMS systemalso provides a temperature measured in close contact with the tirematerial, the direct temperature measurement value can be applied toeliminate the temperature dependence on another parameter (anotherinfluence parameter in the resonance characteristic). For example, byanalyzing the resonance for additional parameter dependence, after usingthe one or more parameters precisely and directly measured by the directTPMS system, the identified resonance effects that are caused by thetire can be used to extract additional information about the state ofthe tire. In some embodiments, information related to a state of thematerial of the tire can be extracted. In an embodiment, estimations ofa state of material age can be determined. In another embodiment,estimations of the thickness of the tire profile can be determined. Inembodiments, because the dependence of the resonance behavior on theexemplary state of material age or tire profile thickness may berelatively small, the evaluation time can be long. Therefore, inembodiments, the parameters that are accessible are typically ones thatchange slowly.

In embodiments, in the case that the temperature provided by the directsensor is not the real tire material temperature, a correction factorfrom the measured temperature to the material temperature can becalculated instead of evaluating another tire parameter.

Referring to FIG. 1A, a block diagram of an embodiment of a tirecharacterization system 10 that utilizes RFA in accordance with anembodiment is depicted. According to an embodiment, system 10 comprisescharacterization engine 12, direct TPMS 14, and indirect TPMS 16. In anembodiment, characterization engine 12 interfaces with direct TPMS 14and indirect TPMS 16. Such an interface, such as a wired or wirelessconnection, can be provided between direct TPMS 14 and indirect TPMS 16so as to operably couple direct TPMS 14 and indirect TPMS 16.

Characterization engine 12 generally includes, in an embodiment, aprocessor and memory. The characterization engine processor can be anyprogrammable device that accepts digital data as input, is configured toprocess the input according to instructions or algorithms, and providesresults as outputs. In an embodiment, the characterization engineprocessor can be a central processing unit (CPU) configured to carry outthe instructions of a computer program. In other embodiments, thecharacterization engine processor can be a digital signal processor(DSP). The processor is therefore configured to perform basicarithmetical, logical, and input/output operations.

The characterization engine memory can comprise volatile or non-volatilememory operably coupled to the characterization engine processor to notonly provide space to execute the instructions or algorithms, but toprovide the space to store the instructions themselves. In embodiments,volatile memory can include random access memory (RAM), dynamic randomaccess memory (DRAM), or static random access memory (SRAM), forexample. In embodiments, non-volatile memory can include read-onlymemory, flash memory, ferroelectric RAM, hard disk, floppy disk,magnetic tape, or optical disc storage, for example. The foregoing listsin no way limit the type of memory that can be used, as theseembodiments are given only by way of example and are not intended tolimit the scope of the claims.

Direct TPMS 14 can include, in an embodiment, sensor 18. Sensor 18 canbe any sensor or plurality of sensors configured to measure a value of atire parameter. For example, sensor 18 can comprise a tire pressuresensor configured to measure the pressure of the tire. In embodiments,direct TPMS 14 can include additional or other sensors, such as one ormore temperature sensors or one or more vibration sensors. Sensor 18 canbe internal or external to the tire and can be mounted in or on, affixedto, embedded in, or otherwise coupled to the tire. In other embodiments,sensor 18 can be mounted proximate but not in or on the tire, such as ona rim, wheel, axle, vehicle body or other suitable place.

Indirect TPMS system 16 is or can include, in an embodiment, wheel speedsignals for example used for an anti-lock braking system (ABS) coupledto an electronic control unit (ECU) that may comprise circuitry and/orcontrollers to process the sensed signals using a resonance frequencyanalysis (RFA) technique. The wheel speed sensor signals may for examplebe generated by a magnetic sensor mounted close to the wheel axis andsensing the varation of a magnetic field caused by a rotation of thewheel axis. In some embodiments, a sensor, rather than an indirect TPMSsystem can be configured to provide the sensed signal(s). In someembodiments, additional information from other sensed signals or storeddata settings for non-tire variables and parameters can be incorporatedinto the RFA. In embodiments, the RFA uses a spectral analysis of tirevibrations as determined from the sensed ABS signals over differentpoints in the spectrum that can reflect different vibration modes anddifferent corresponding resonance frequencies. In other embodiments, theRFA approach can look for a single resonance frequency. In someembodiments, an enhanced transmission protocol for the sensed ABSsignals to the ECU for analysis by the ECU can result in an increasedsignal to noise ratio (SNR) for the RFA technique, thereby enablingidentification of potential resonance frequencies that might otherwisefall below a conventional noise threshold.

In various embodiments, the sensed data signals can include higherresolution data from the ABS. The different vibration modes can includeradial vibration, angular vibration, and other types of tire, wheel ordrive train vibrations, as well as higher order harmonics of thesevibrations, and can be optimized for RFA of multiple physical variablesassociated with the tire in addition to tire pressure. Examples of suchadditional tire variables that can impact different resonance frequencymodes include tire speed, temperature, thickness, size, profile, wear,age, and materials, among others. Other non-tire related variables canalso be utilized in the RFA and can include ambient conditions; vehicledriving data related to acceleration, turning and braking maneuvers; andvehicle condition parameters including weight, equipment options, andfeature settings such as suspension modes or traction control, amongothers.

In general, wheel speed corresponds to the first order frequency of thesensed signal from the ABS, with the wheel speed corresponding to aduration between pulses. In one embodiment, there are about 48 pulsesper rotation, which provides good granularity. Changes in thesecharacteristics can then be analyzed to determine whether any areindicative of a change in the pressure of the tire.

For example, an indirect TPMS can detect a change in resonance frequencyof a tire. A decrease of the resonance frequency could be indicative ofa lower tire pressure in the tire. Information then can be transmittedfrom indirect TPMS to other circuitry associated with the TPMS orvehicle ECU in several manners. In one embodiment, the sensed signalsand optional non-tire variables and parameters are transmitted to andanalyzed by an electronic control unit (ECU). In another embodiment,other kinds of sensed signals and optional non-tire variables andparameters can be communicated to an ECU or other vehicle microprocessoror controller system for utilization in the RFA technique. Any of thesesignals, variable or parameters, as well as the resonance frequencies ofeach tire may be communicated to other vehicle microprocessor orcontroller systems for utilization by the vehicle in other operationsand/or display for the vehicle operator.

The RFA performed by the ECU, vehicle microprocessor and/or othercontroller circuitry can be carried out by a variety of circuit,controller, and microprocessor components that are programmed orconfigured to perform the RFA as described herein, either in a singlecomponent or with various portions of the RFA performed by differentcomponents in coordination with each other. The RFA can be performedbased on digital data and digital techniques, including the use of adigital signal processor (DSP), analog data and analog techniques, orany combination thereof. In various embodiments, the systems and methodsfor performing the RFA can accomplish the RFA based on various analysistechniques that can include DSP analysis, auto-regression analysis,discrete Fourier transform, wavelet transform, Gabor transform, FastFourier analysis, digital or analog filter banks, a Bayesian analysis, Qfactor analysis, harmonic analysis and/or analysis based on a linearestimation model, among others and alone or in combination.

Referring to FIG. 2, a schematic diagram of an embodiment of an indirectTPMS wheel sensor system 100 that utilizes RFA in accordance with anembodiment is depicted. Wheel 110 is shown with a tire 112 and an ABSsystem 120. The particular relative positions of ABS system 120 andwheel 110 are merely exemplary and can vary in embodiments. Moreover,components in this and other figures herein are not necessarily drawn toscale. Because wheel 110 is a complex structure, there should be amultitude of resonances in response to complex vibrations occurringduring movement. Embodiments utilize these multiple resonances to locateand isolate resonance peaks of different resonance modes, e.g., a radialvibration, r, and an angular vibration, ω. Thus, tire 112 can be modeledas a complex arrangement of mechanical resonators, δ_(ω) and m_(ω) forangular vibration, and δ_(r) and m_(r) for radial vibration. In someembodiments, the vibration of tire 112 can be modeled as atwo-dimensional model of mechanical resonators, while in otherembodiments, tire 112 can be modeled as a three-dimensional model ofmechanical resonators.

In some embodiments, the information sensed from the vibrations of thetire 112, such as by one or more sensors 114 mounted in or on, affixedto, embedded in, or otherwise coupled to tire 112, can be analyzed for amultidimensional resonance frequency within the ABS system 120. In otherembodiments, one or more sensors 114 can be mounted proximate but not inor on tire 112, such as on a rim, wheel, axel, vehicle body or othersuitable place, though sensors so positioned may not be able to senseactual tire characteristics, such as material temperature. In someembodiments, the digitized information can be modulated onto theconventional ABS wheel speed clock signal generated by, for example, anencoder tooth wheel 122, for transmission to and analysis by anelectronic control unit (ECU). According to embodiments, additionalinformation about multidimensional resonances of the sensed signal thatcan include higher-order harmonics of the wheel rotation can be providedto the ECU, which can then be utilized while reducing warning latency,thereby providing a more robust system that balances provision of earlywarnings with false alarms. In other embodiments, a sensor or series ofsensors, rather than components of the indirect TPMS system can beconfigured to provide the sensed signal(s).

Referring to FIG. 3, a block diagram is depicted of an indirect TPMSwheel sensor system 200 in accordance with an embodiment. System 200 isor comprises an ABS system 220 including a speed sensor 222 inembodiments. For example, system 200 can comprise an ABS system 220 withadditional circuitry and/or algorithms in order to process indirect TPMSdata in one embodiment, or system 200 can comprise additional circuitry,algorithms and/or other sense and control components 240 external to ABSsystem 220 to carry out the processing of the indirect TPMS data. Forexample, one or more sensors 114 can comprise components 240, or thosesensors 114 can be considered to be part of indirect TPMS 200, with orwithout additional sensors as part of components 240. In one embodiment,the additional circuitry and/or algorithms can be part of an ECU 230 orsignal processing system, though they need not be in all embodiments.Various additional data sensing and control system components 240 canalso be provided in embodiments and can comprise one or more of anaccelerometer sensor and/or system, an inertia sensor or sensor cluster,an ambient environment sensor and/or system and a vehicle controlsystem, each of which can include various sensors and controlarrangements known in the art to provide global vehicle parameter data,such as one or more of sensed data, operational data and/or controlparameters for the vehicle. In another embodiment, characterizationengine 22 can be incorporated into one or more components of wheelsensor system 200, and for example, ECU 230.

Referring to FIG. 4, a block diagram of ABS sensor circuitry 300 inaccordance with one embodiment is depicted. Circuitry 300 includes ananalog portion 302 and a digital portion 304 coupled by ananalog-to-digital (ADC) converter 306. Analog portion 302 comprises oneor more Hall probes 308 or other magnetic field sensors, offsetcompensation circuitry 310 and gain circuitry 312. Digital portion 304comprises maximum/minimum detection circuitry 314, zero-crossingdetection circuitry 316, optional frequency analysis circuitry 318,optional analysis of harmonics circuitry 320 and pulse forming circuitry322. Optional frequency analysis circuitry 318 and analysis of harmonicscircuitry 320 form part of an indirect TPMS 324, in an embodiment. Ifnone of the optional analysis or interface options are used, circuitry300 represents one possible embodiment of a standard ABS system. Thedepiction of system 300 is merely exemplary, and more or fewercircuitries, sensors and other components can be implemented in otherembodiments. Moreover, the block diagram of FIG. 4 can be consideredfunctional, such that some blocks depicted as being distinct can in factbe combined in actual implementation. Additionally, blocks depicted asbeing part of other blocks can, in other embodiments, be distincttherefrom or be part of other blocks, whether specifically depicted ornot.

In one embodiment, information from indirect TPMS 200 is represented ina digital frame protocol format and is modulated onto the original ABSclock signal by adapting the pulse length to the state of the relatedbit of the frame. Thus, the ABS wheel speed signal is represented by therising edge while the TPMS information is in the pulse duration of asequence of pulses. Co-owned and co-pending U.S. application Ser. No.13/751,335, entitled “A SIGNAL GENERATOR, A DECODER, A METHOD FORGENERATING A TRANSMIT SIGNAL AND METHODS FOR DETERMINING SPEED DATA,”filed Jan. 28, 2013, discloses additional information regardingcommunicating data between a sensor and an ECU or other control orprocessing system and is incorporated herein by reference in itsentirety. As disclosed therein, a signal generator includes a signalprovider and a signal processing unit. The signal provider is configuredto provide a sensor signal indicating a repeatedly detected event,occurring within differing time intervals. The signal processing unit isconfigured to generate a transmit signal based on the sensor signal. Thetransmit signal includes event information representing the temporaloccurrence of the event and additional information representingadditional data. The event information includes pulses or signal edgesassociated to detected events, wherein the pulses or signal edges aretemporarily separated within the transmit signal according to thediffering time intervals of detected events so that each time intervalof the differing time intervals includes one pulse or one signal edgeassociated with a detected event. Further, the additional data includesat least one frame including a predefined number of additional databits. The information of the additional data bits of the at least oneframe is distributed over at least two time intervals of the differingtime intervals.

In one embodiment, information from indirect TPMS 200 is represented inanalog form and transmitted as analog information. The magnetic fieldresulting from a rotating a magnetic pole wheel or magnetic tooth wheelis measured by a speed sensor and converted to a suitable protocol andprovided to an ECU as the output signal having a speed data portion andan enhanced resonance data portion. Co-owned and co-pending U.S. patentapplication Ser. No. 13/903,088 entitled “WHEEL SPEED SENSOR ANDINTERFACE SYSTEMS AND METHODS,” filed May 28, 2013, discloses additionalinformation regarding communicating data between a sensor and an ECU orother control or processing system and is incorporated herein byreference in its entirety. The speed sensor is herein configured todetect a magnetic field in response to speed and resonancecharacteristics. The speed sensor is also configured to generate asensor output signal having speed data and enhanced resonance data whichis received by the sensor output signal. The speed signal which ismeasured in the speed sensor and delivered as analog value can also beused as redundant information to the speed information that is encodedin the speed pulses of the classic ABS protocol. A sensor output signalmay be generated having speed data and enhanced resonance data byselecting a first current level and a second current level, generating amagnetic field in response to a rotation that is influenced by tirevibrations, generating a field sensor output from the magnetic field andgenerating a sensor output signal from the field sensor output accordingto the selected first current level and the second current level,wherein the sensor output signal includes speed data and enhancedresonance data. A measurement system used in the indirect TPMS maycomprise a magnetic field sensor configured to measure a magnetic fieldand to generate a field sensor output, a summation component configuredto combine an offset with the field sensor output to provide a modifiedsensor output and a current modulation component configured to generatea sensor output signal from the modified sensor output, the sensoroutput signal having speed data and enhanced resonance data.

In another embodiment, a separate communication source, such as a wiredor wireless connection, can be provided between indirect TPMS 200 andECU 230. For example, a two- or three-wire connection between sensor 222and/or other sensors of system 200 to ECU 230 can be used, which canprovide an amplified and decoupled version of the analog sensor outputas it enters ADC 306 or for some other digital signal or message fromthe ADC 306, FFT (e.g., circuitry 318) or resonance analysis. Several ofthese optional couplings are depicted in FIG. 4, and in variousembodiments one or more of them can be used or omitted, and/oradditional ones can be added, such that the particular depiction in FIG.4 is merely exemplary of one of a variety of possibilities.

Referring to FIG. 5, a graphical representation of a spectrum of a wheelspeed signal according to an embodiment is shown for the sensed signaldata representing wheel speed. Curve 400 represents the wheel speedspectral density based on actual average wheel speed. In this example,curve 400 two tire vibration modes are depicted. A first tire vibrationmode is indicated at 410 and a second tire vibration mode is indicatedat 420. In this example, the first vibration mode 410 has a weakdependence upon tire pressure and profile thickness, but a strongerdependence upon tire temperature and state of material age. The secondvibration mode 420 has a strong dependence upon tire pressure, a strongdependence upon tire temperature, a strong dependence upon profilethickness, but a weak dependence upon state of material age.

In an embodiment, noise floor 430 is shown in dotted line and is higherthan the floor of curve 400 of the wheel spectral density. Noise floor430 illustrates that it can be difficult to recognize the firstvibration mode 410 in the spectrum because it is almost covered by thenoise. However, second vibration mode 420 is readily recognizable inthis spectrum and still can be dependent on pressure and anotherparameter of interest, as described above. Therefore, second vibrationmode 420 can be used to estimate the parameter of interest if, forexample, pressure is known from the direct TPMS, such as in direct TPMS14.

Here instead of using the influence of different parameters on differentresonance frequencies, the absolute height of the resonance peaks or thequality factor of the resonance can be taken into account as well, whichincreases the diversity of the available effects, e.g., the pressureinside the tire can have a higher influence on the location of theresonance frequency while, e.g., the temperature of the rubber couldhave more influence on the internal friction and therefore cause asignificant change of the damping and thus finally on the Q-factor of aresonance. The first resonance 410 is mainly influenced by parameters ofthe tire (e.g. a deformation that does not significantly change thevolume) and therefore shows low pressure dependence but strongtemperature dependence, for example. The second resonance is a pressuredependent effect (e.g., deformation of the tire cross-section thatchanges the volume) and is heavily temperature dependent as well. If atleast one measurement in the frequency range of each vibration mode isavailable, the influencing parameters can be separated by use ofequations that describe the dependence of the two measurements on eachparameter, and the equations can be resolved for the independentparameters. For example, the strong temperature dependence of both modes410 and 420 can be removed and the change in pressure isolated betweenmodes 410 and 420. In embodiments, then, the tire pressure measureddirectly by, for example, sensor 18 of direct TPMS 14 can be includedsuch that the influence of pressure is removed from the resonanceanalysis.

In other embodiments, additional characteristics can be used to isolatethe pressure or other parameters, based on their respective dependenceon other characteristics. If the dependence of the measurements on theindependent parameters is too complex and/or the pressure cannot beisolated, a numerical meta model of the tire can be established based ona characterization of a tire type over variations of the independentparameters.

Additional analysis or inclusion of additional sensed data or parametersmay be used to identify or isolate further vibrational resonance modesin the wheel speed data. For example, different measurements that can beutilized to extract the different influencing parameters could bemultitudes or combinations of the following: a) frequency (location) ofa resonance of the wheel speed, b) spectral density of the wheel speedat a resonance (maximum), c) spectral density of the wheel speed at aminimum, d) frequency (location) of a minimum, d) spectral density ofthe wheel speed in a fix distance from a maximum or minimum, e) Q factorof a resonance peak, f) spectral density of the wheel speed at a fixdefined frequency, g) any of the previous measurements that are selecteddepending on the type or dimension of the tire, g) any of the previousmeasurements that are selected or interpreted depending on the actualspeed of the observed wheel, h) any of the previous measurements thatare selected or interpreted depending on accessible vehicle parameters(e.g. speed of the car, acceleration, load and its distribution,actuation of vehicle control systems (steering, breaking, power train,vehicle stability control, active damper, global chassis control), i)any of the previous measurements that are selected depending on alreadyacquired measurement points, j) any of the previous measurementsselected depending on already evaluated parameters (e.g. tire pressureor tire temperature), and/or k) non-tire related sensed data (e.g.,ambient temperature, humidity), in various embodiments.

Based on the new system for the spectral analysis inside the sensorusing high resolution data, it can be possible to locate more than justa small pressure dependent change of the analyzed spectrum that exceedsthe noise floor. Embodiments, however, are not restricted to being usedwith a spectral analysis of the sensor device. For example, in otherembodiments the noise sensitivity of the generated pulses can bereduced, including significantly, by one or more of the followingtechniques: reducing a distance between the sensor and pole wheel orother target in order to have a higher anticipated magnetic fieldstrength; providing a stronger magnetization of the pole wheel or othertarget itself; implementing new or different sensing techniques, such asmagnetoresistive (xMR) techniques including tunneling MR, TMR; and/orusing higher quality, more precise, or other improved circuitry, thoughsuch circuitry may be balanced with, e.g., increased power consumptionand other factors. In general, vibrations depend on multiple physicalinfluences, with the tire pressure being only one, and others caninclude the temperature of the rubber material or the thickness of theprofile. Furthermore, there typically is an influence from the aging ofthe rubber material that changes its flexibility due to a loss ofsoftening plasticizer depending on time and ambient conditions. Sincethe different resonance modes represent different deformations of thetire during the vibration, it can be assumed that the influence of eachparameter on each resonance is different. If multiple resonance effectsare identified, these can be used to isolate the influence of thedifferent parameters on tire pressure. In other embodiments, it can bepossible to recognize the type of tire by comparison of the multipleresonance effects with known resonance signatures of tires based onsize, model, materials and/or manufacturer. Or, conversely, data can beobtained and models built for each tire and/or each tire/vehiclecombination and then stored in a memory of system 100 for use duringoperation.

Referring again to system 10 as depicted in FIG. 1A, characterizationengine 12 interfaces with direct TPMS 14 and indirect TPMS 16. In anembodiment, characterization engine 12 is configured to receive tirepressure data from direct TPMS 14, receive data representative of wheelspeed of a tire from indirect TPMS 16, analyze the indirect TPMS 16 datato determine a plurality of resonance frequencies associated with thedata, incorporate the tire pressure data to remove the influence ofpressure on the plurality of resonance frequencies, and calculate one ormore tire characteristics based on the plurality of resonancefrequencies. In embodiments, other sensor 18 data can be received fromdirect TPMS 14 in order to eliminate additional influence parameters inthe plurality of resonance frequencies.

Referring to FIG. 1B, a block diagram of another embodiment of a tirecharacterization system 20 that utilizes RFA in accordance with anembodiment is depicted. Characterization system 20 generally comprises adirect TPMS 24 and an indirect TPMS 26. Direct TPMS 24 and indirect TPMS26 are substantially similar to direct TPMS 14 and indirect TPMS 16 asdescribed above, except that characterization engine 22, which issubstantially similar to characterization engine 12, can be implementedas a component of indirect TPMS 26. In such embodiments, indirect TPMS16 and direct TPMS 24 are configured to interface with each other inorder to incorporate the direct measurements of sensor 28 into thesystem of equations calculated by the RFA analysis of indirect TPMS 26.

Thus, in one embodiment and referring to FIG. 6, a method 500 ofcharacterizing at least one parameter on a vehicle includes obtaining awheel speed signal at 502. At 504, at least one vibration mode in thewheel speed signal is identified. Then, at 506, one or morecharacteristics influencing the vibration mode, including at least onecharacteristic of interest (e.g., tire pressure), are identified and, at508, resolved to isolate at least one individual parameter of interest.For example, an unknown (without incorporation of direct TPMS data)parameter that influences the resonance can be isolated. In embodiments,at least two vibration modes in the wheel speed signal are identified.

In an optional embodiment, at 509, a functional safety check can beperformed. In an embodiment, the preliminarily-resolved at least oneindividual characterstic of interest can be compared to itscorresponding direct measurement for validity analysis or other analysischeck or confirmation in order to determine the reasonableness of thecorresponding direct measurement. If the direct measurement isdetermined to be unreasonable compared to the preliminarily-resolvedcharacterstic of interest, the indirect TPMS can proceed to an errorstate or otherwise abort the instant calculation. If the directmeasurement is determined to be reasonable compared to thepreliminarily-resolved characterstic of interest, the method 500 ofcharacterizing can proceed to 510. For example, the direct TPMS tirepressure can be verified as consistent or reasonable with an indirectTPMS tire pressure estimate and be incorporated into the method 500 ofcharacterizing. If inconsistent or unreasonable, the direct TPMS tirepressure can be unincorporated or trigger the method to proceed to anerror state, as appropriate. In an embodiment, reasonableness orunreasonableness can be determined by an absolute value difference. Inother embodiments, other difference calculations can be performed. In anembodiment, therefore, the direct TPMS system provides redundancy withthe indirect TPMS system.

At 510, the one or more direct measurements from sensor 18 of directTPMS 14, for example, in system 10 of FIG. 1A, can be incorporated intothe system of equations for the isolated at least one individualparameter of interest. For example, the direct tire pressure measurementcan be input for the resolved at least one characteristic of interest.At 512, one or more additional parameters can then be characterizedafter the inclusion of the one or more direct measurements. The one ormore additional parameters can be characterized based on, for example,analysis of the characteristics influencing the vibration modes. In oneembodiment, the influence of the various characteristics can be derivedby a system of equations. In other embodiments, additional sensorsignals and information can be used, e.g., other signals considered at502.

In a feature and advantage of embodiments, systems and methods canestimate one or more parameters of a tire that would not be accessiblein a RFA analysis without a direct TPMS measurement or measurements. Forexample, in an embodiment, the temperature of the tire material can beestimated, wherein it is typically difficult to directly measure suchmaterial. In other embodiments, other tire parameters can be estimated,such as state of material age or profile thickness.

In another feature and advantage of embodiments, systems and methodsallow for the comparison of RFA-estimated parameters (from, for example,an indirect TPMS) against an actual measured parameter (from, forexample, a direct TPMS). Such comparison can be useful as part of afailure analysis or functional safety analysis, for example.

Various embodiments of systems, devices and methods have been describedherein. These embodiments are given only by way of example and are notintended to limit the scope of the invention. It should be appreciated,moreover, that the various features of the embodiments that have beendescribed may be combined in various ways to produce numerous additionalembodiments. Moreover, while various materials, dimensions, shapes,configurations and locations, etc. have been described for use withdisclosed embodiments, others besides those disclosed may be utilizedwithout exceeding the scope of the invention.

Persons of ordinary skill in the relevant arts will recognize that theinvention may comprise fewer features than illustrated in any individualembodiment described above. The embodiments described herein are notmeant to be an exhaustive presentation of the ways in which the variousfeatures of the invention may be combined. Accordingly, the embodimentsare not mutually exclusive combinations of features; rather, theinvention can comprise a combination of different individual featuresselected from different individual embodiments, as understood by personsof ordinary skill in the art. Moreover, elements described with respectto one embodiment can be implemented in other embodiments even when notdescribed in such embodiments unless otherwise noted. Although adependent claim may refer in the claims to a specific combination withone or more other claims, other embodiments can also include acombination of the dependent claim with the subject matter of each otherdependent claim or a combination of one or more features with otherdependent or independent claims. Such combinations are proposed hereinunless it is stated that a specific combination is not intended.Furthermore, it is intended also to include features of a claim in anyother independent claim even if this claim is not directly madedependent to the independent claim.

Any incorporation by reference of documents above is limited such thatno subject matter is incorporated that is contrary to the explicitdisclosure herein. Any incorporation by reference of documents above isfurther limited such that no claims included in the documents areincorporated by reference herein. Any incorporation by reference ofdocuments above is yet further limited such that any definitionsprovided in the documents are not incorporated by reference hereinunless expressly included herein.

For purposes of interpreting the claims for the present invention, it isexpressly intended that the provisions of Section 112, sixth paragraphof 35 U.S.C. are not to be invoked unless the specific terms “means for”or “step for” are recited in a claim.

What is claimed is:
 1. A tire characterization system, comprising: asensor configured to provide a wheel speed signal; a direct tirepressure monitoring system (TPMS) including at least one sensorconfigured to provide a direct measurement of a characteristic of thetire; and a processing unit configured to process the wheel speed signalto identify at least one tire vibration mode and use the identified atleast one tire vibration mode and the direct measurement to estimate acharacterized parameter of the tire.
 2. The tire characterization systemof claim 1, wherein the sensor configured to provide a wheel speedsignal is a component of an indirect tire pressure monitoring system(TPMS).
 3. The tire characterization system of claim 1, wherein theprocessing unit is configured to process the wheel speed signal by usinga resonance frequency analysis (RFA) that includes a spectral analysisfor identifying the at least one tire vibration mode in the wheel speedsignal that depends on at least two unknown parameters of the tire, andwherein the processing unit is configured to process the directmeasurement by removing at least one of the unknown parameters in orderto estimate the characterized parameter, the characterized parameterbeing a non-measured unknown parameter that influences the resonance. 4.The system of claim 2, wherein the indirect tire pressure monitoringsystem (TPMS) further comprises: an antilock braking system (ABS)configured to provide the wheel speed signal; and an electronic controlunit (ECU) coupled to the ABS and configured to process the sensed wheelspeed signal, wherein at least one of the processing unit or theindirect TPMS is a component of the ECU.
 5. The system of claim 3,wherein the sensor is a pressure sensor.
 6. The system of claim 5,wherein one of the at least two unknown parameters of the tire is tirepressure and the characterized parameter is tire material temperature.7. The system of claim 3, wherein the processing unit is furtherconfigured to isolate a second of the at least two unknown parametersthat influences the resonance and process the direct measurement toremove the second unknown parameter as an unknown parameter in theestimation of the characterized parameter of the tire.
 8. The system ofclaim 7, wherein the unknown parameter is tire pressure, the secondunknown parameter is tire material temperature, and the characterizedparameter is one of state of material age or tire profile thickness. 9.The system of claim 3, wherein the processing unit is further configuredto compare the removed at least one unknown parameter to thecorresponding direct measurement in order to determine a reasonabilityof the direct measurement prior to estimation of the characterizedparameter.
 10. A method of characterizing at least one parameter of avehicle tire, the method comprising: obtaining at a processing unit datarepresentative of a wheel speed of the tire from a sensor; analyzing thedata using the processing unit; obtaining at the processing unit adirect measurement from at least one sensor corresponding to the unknownparameter; incorporating the direct measurement into the analyzed datato remove the unknown parameter using the processing unit; andcharacterizing a parameter of the vehicle tire using the analyzed datathat incorporated the direct measurement using the processing unit. 11.The method of claim 10, wherein analyzing the data comprises using asignal processing system to determine at least one resonance parameterassociated with the data, wherein the method further comprises analyzingthe at least one resonance parameter using the signal processing systemto isolate an unknown parameter, and wherein incorporating the directmeasurement into the analyzed data comprises incorporating the directmeasurement into the at least one resonance parameter.
 12. The method ofclaim 11, wherein the at least one sensor for obtaining the directmeasurement is a pressure sensor, and wherein the unknown parameter istire pressure and the characterized parameter is tire materialtemperature.
 13. The method of claim 11, wherein obtaining datarepresentative of wheel speed of the tire comprises extracting data froman antilock braking system (ABS) for a tire.
 14. The method of claim 11,further comprising: analyzing the at least one resonance parameter toisolate a second unknown parameter; obtaining a second directmeasurement from a second at least one sensor corresponding to thesecond unknown parameter; and incorporating the second directmeasurement into the at least one resonance parameter to remove thesecond unknown parameter.
 15. The method of claim 14, wherein theunknown parameter is tire pressure, the second unknown parameter is tirematerial temperature, and the characterized parameter is a state ofmaterial age or a tire profile thickness.
 16. The method of claim 11,wherein the at least one resonance parameter comprises one of aresonance frequency, a resonance peak height, and a 0-factor of theresonance.
 17. The method of claim 11, further comprising comparing theunknown parameter to the direct measurement using the processing unit inorder to determine a reasonability of the direct measurement.
 18. A tirecharacterization system comprising: at least one sensor configured toprovide at least a direct measurement of a characteristic of the tire; awheel speed sensor configured to provide a wheel speed signal; and anelectronic control unit (ECU) coupled to the wheel speed sensor andconfigured to: process the wheel speed signal, to identify at least onetire vibration mode associated with the processed wheel speed signal,and process the direct measurement and the identified at least one tirevibration mode in order to estimate a characterized parameter of thetire.
 19. The tire characterization system of claim 18, wherein the ECUis configured to process the sensed wheel speed signal using a resonancefrequency analysis (RFA) that includes a spectral analysis identifyingthe at least one tire vibration mode in the wheel speed signal, whereinthe ECU is further configured to isolate at least one unknown parameterthat influences a resonance, and wherein the ECU is further configuredto process the direct measurement by removing the at least one unknownparameter that influences the resonance in order to estimate thecharacterized parameter.
 20. The tire characterization system of claim19, wherein the at least one sensor is a pressure sensor, and whereinthe unknown parameter is tire pressure and the characterized parameteris tire material temperature.